Real-time prediction of smoking activity using machine learning based multi-class classification model

• Published in: Multimedia tools and applications Vol. 81; no. 10; pp. 14529 - 14551
• Main Authors: Thakur, Saurabh Singh, Poddar, Pradeep, Roy, Ram Babu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Feature extraction; Health care; Inertial platforms; Inertial sensing devices; Just in time; Machine learning; Multimedia Information Systems; Real time; Sensors; Smoking; Special Purpose and Application-Based Systems; Wearable computers; Wearable technology; Wrist; Smoking cessation; Preventive healthcare; Multimedia applications; Personalized healthcare; mHealth; Predictive modeling; Wearable sensors; IoT
• ISSN: 1380-7501; 1573-7721; 1573-7721
• DOI: 10.1007/s11042-022-12349-6

Smoking cessation efforts can be greatly influenced by providing just-in-time intervention to individuals who are trying to quit smoking. Detecting smoking activity accurately among the confounding activities of daily living (ADLs) being monitored by the wearable device is a challenging and intriguing research problem. This study aims to develop a machine learning based modeling framework to identify the smoking activity among the confounding ADLs in real-time using the streaming data from the wrist-wearable IMU (6-axis inertial measurement unit) sensor. A low-cost wrist-wearable device has been designed and developed to collect raw sensor data from subjects for the activities. A sliding window mechanism has been used to process the streaming raw sensor data and extract several time-domain, frequency-domain, and descriptive features. Hyperparameter tuning and feature selection have been done to identify best hyperparameters and features respectively. Subsequently, multi-class classification models are developed and validated using in-sample and out-of-sample testing. The developed models obtained predictive accuracy (area under receiver operating curve) up to 98.7% for predicting the smoking activity. The findings of this study will lead to a novel application of wearable devices to accurately detect smoking activity in real-time. It will further help the healthcare professionals in monitoring their patients who are smokers by providing just-in-time intervention to help them quit smoking. The application of this framework can be extended to more preventive healthcare use-cases and detection of other activities of interest.